This server exhibits significant quality gaps across naming, descriptions, and schema completeness. The ETL tools (1-8) have reasonable verb-based names and basic descriptions, but lack parameter-level detail and output schema documentation. Tools 9-17 are severely underdocumented: most are single-parameter 'question' or 'input_data' wrappers around CrewAI agents with minimal guidance on what they actually do, when to use them, or what they return. No tool includes output schema documentation. Error handling is absent across all tools. Security concerns exist (eval() in transform_data, no input validation). The server mixes ETL utilities with agent proxies without clear composition or chaining support.

• Add explicit output schema documentation to every tool. For ETL tools, document the structure of returned dicts (e.g., 'Returns {"data": List[Dict], "columns": List[str], "row_count": int}'). For agent tools, document what fields the CrewAI agent returns.
• Replace agent proxy tools with specific, well-named tools that document their domain. Instead of 'multi_analyst' (ambiguous), create 'query_financial_data', 'query_database', etc. Each should have a clear description of what domain it covers and when to use it.
• Add JSON Schema constraints to complex parameters. For type_mapping, strategy, rules, constraints, and transformation_rules, define the expected structure (e.g., type_mapping: {type: 'object', additionalProperties: {type: 'string', enum: ['int', 'str', 'float', 'bool']}}). This enables validation and guides LLM input.
• Replace eval() in transform_data with a safe expression evaluator (e.g., numexpr or a restricted AST-based evaluator). Document the allowed operations and syntax in the parameter description.
• Add error recovery guidance to all tools. When type conversion fails, return 'Column "age" cannot convert "abc" to int. Provide a valid numeric value or use handle_missing_values() to drop/fill the column.' When constraints fail, suggest remediation steps.